Impairment of alertness in vehicle and machinery operators poses a danger not only to themselves but also often to the public at large. Thousands of deaths and injuries result each year that are fatigue related. Moreover, the financial costs as a result of the injuries and deaths are prohibitive. As a result, significant efforts have been made in the area of vigilance monitoring of the operator of a vehicle to detect the decrease in attention of the operator and to alert her/him.
Existing driver vigilance monitoring techniques fall within the following broad classes: (1) image acquisition and processing of facial features such as eye and head movements; (2) image acquisition and processing of road lane maintaining capability and (3) monitoring of the physiological responses of the body while driving.
There are several limitations with the existing technologies. Although the most accurate of the vigilance monitoring techniques, monitoring physiological responses of the body like EEG and ECG, are intrusive, expensive and can distract or cause annoyance to the driver. Moreover, lane tracking methods require visible lane markings and even if present are significantly impaired by snow, rain, hail, and/or dirt existing on the road. Nighttime and misty conditions are also impairments. Image processing techniques used to track eyes and head patterns to track lane maintaining capability necessarily require expensive (both cost and computational requirement-wise) hardware to operate and are highly dependent on factors such as, the relative position of the driver's head with respect to the sensors, illumination, and facial features and/or mental state of the driver, whether happy, anxious, or angry. Each of these indicators suffers from a relatively low probability of detecting drowsiness. Many of the measurements for indicating drowsiness do not adequately represent the responsiveness of the driver because of such influences as road conditions, patterns and vehicle type. Moreover, the cost for these techniques is often prohibitive. Even yet, more often than not, the existing techniques detect drowsiness when it may be too late for accident prevention purposes.
Therefore, there has been a longstanding and continuing need for a system and method that detects drowsiness using parameters that are independent of the driving environment, such as traffic, landscape, weather, darkness, road conditions, patterns and vehicle type. Additionally, there is also a need for a driver vigilance monitoring system and method that detects drowsiness with very low false alarm and miss-rates without being intrusive, distractive to the driver and/or prohibitive in cost.